Influence maximization in complex networks through optimal percolation.
Nature
; 524(7563): 65-8, 2015 Aug 06.
Article
em En
| MEDLINE
| ID: mdl-26131931
The whole frame of interconnections in complex networks hinges on a specific set of structural nodes, much smaller than the total size, which, if activated, would cause the spread of information to the whole network, or, if immunized, would prevent the diffusion of a large scale epidemic. Localizing this optimal, that is, minimal, set of structural nodes, called influencers, is one of the most important problems in network science. Despite the vast use of heuristic strategies to identify influential spreaders, the problem remains unsolved. Here we map the problem onto optimal percolation in random networks to identify the minimal set of influencers, which arises by minimizing the energy of a many-body system, where the form of the interactions is fixed by the non-backtracking matrix of the network. Big data analyses reveal that the set of optimal influencers is much smaller than the one predicted by previous heuristic centralities. Remarkably, a large number of previously neglected weakly connected nodes emerges among the optimal influencers. These are topologically tagged as low-degree nodes surrounded by hierarchical coronas of hubs, and are uncovered only through the optimal collective interplay of all the influencers in the network. The present theoretical framework may hold a larger degree of universality, being applicable to other hard optimization problems exhibiting a continuous transition from a known phase.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Rede Social
/
Modelos Teóricos
Tipo de estudo:
Prognostic_studies
Limite:
Humans
País/Região como assunto:
Mexico
Idioma:
En
Revista:
Nature
Ano de publicação:
2015
Tipo de documento:
Article
País de afiliação:
Estados Unidos
País de publicação:
Reino Unido